{
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   "source": [
    "Finding Critical Points using RNNs\n",
    "\n",
    " - [Data Prep notebook](https://www.kaggle.com/code/werus23/sleep-critical-point-prepare-data)\n",
    " - Training notebook - this\n",
    " - [Inference Notebook](https://www.kaggle.com/code/werus23/child-sleep-critical-point-regression)\n",
    "\n",
    "Credits:\n",
    "\n",
    " - idea: https://www.kaggle.com/competitions/child-mind-institute-detect-sleep-states/discussion/441470\n",
    " - dataloader: https://www.kaggle.com/code/henriupton/efficient-loading-memory-usage-visualizations-cmi\n",
    " - arch: https://www.kaggle.com/competitions/tlvmc-parkinsons-freezing-gait-prediction/discussion/416410"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4964be9d",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1. Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2200aeb9",
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    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gc\n",
    "import time\n",
    "import json\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import joblib\n",
    "import random\n",
    "import math\n",
    "from tqdm.auto import tqdm \n",
    "\n",
    "# from scipy.interpolate import interp1d\n",
    "\n",
    "from math import pi, sqrt, exp\n",
    "# import sklearn,sklearn.model_selection\n",
    "import torch\n",
    "from torch import nn,Tensor\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, Dataset, SubsetRandomSampler\n",
    "# from sklearn.metrics import average_precision_score\n",
    "from timm.scheduler import CosineLRScheduler\n",
    "plt.style.use(\"ggplot\")\n",
    "\n",
    "from pyarrow.parquet import ParquetFile\n",
    "import pyarrow as pa \n",
    "import ctypes\n",
    "\n",
    "def normalize(y):\n",
    "    mean = y[:,0].mean().item()\n",
    "    std = y[:,0].std().item()\n",
    "    y[:,0] = (y[:,0]-mean)/(std+1e-16)\n",
    "    mean = y[:,1].mean().item()\n",
    "    std = y[:,1].std().item()\n",
    "    y[:,1] = (y[:,1]-mean)/(std+1e-16)\n",
    "    return y\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46c4f383",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [],
   "source": [
    "EPOCHS = 5\n",
    "WARMUP_PROP = 0.2\n",
    "BS = 1\n",
    "WORKERS = 4\n",
    "TRAIN_PROP = 0.9\n",
    "max_chunk_size = 150000\n",
    "if device=='cpu':\n",
    "    torch.set_num_interop_threads(WORKERS)\n",
    "    torch.set_num_threads(WORKERS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06f9891b",
   "metadata": {
    "_kg_hide-input": true,
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   "source": [
    "def plot_history(history, model_path=\".\", show=True):\n",
    "    epochs = range(1, len(history[\"train_loss\"]) + 1)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(epochs, history[\"train_loss\"], label=\"Training Loss\")\n",
    "    plt.plot(epochs, history[\"valid_loss\"], label=\"Validation Loss\")\n",
    "    plt.title(\"Loss evolution\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.legend()\n",
    "    plt.savefig(os.path.join(model_path, \"loss_evo.png\"))\n",
    "    if show:\n",
    "        plt.show()\n",
    "    plt.close()\n",
    "\n",
    "#     plt.figure()\n",
    "#     plt.plot(epochs, history[\"valid_mAP\"])\n",
    "#     plt.title(\"Validation mAP evolution\")\n",
    "#     plt.xlabel(\"Epochs\")\n",
    "#     plt.ylabel(\"mAP\")\n",
    "#     plt.savefig(os.path.join(model_path, \"mAP_evo.png\"))\n",
    "#     if show:\n",
    "#         plt.show()\n",
    "#     plt.close()\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(epochs, history[\"lr\"])\n",
    "    plt.title(\"Learning Rate evolution\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"LR\")\n",
    "    plt.savefig(os.path.join(model_path, \"lr_evo.png\"))\n",
    "    if show:\n",
    "        plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d824c39a",
   "metadata": {
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    "_kg_hide-output": true,
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   "source": [
    "class ResidualBiGRU(nn.Module):\n",
    "    def __init__(self, hidden_size, n_layers=1, bidir=True):\n",
    "        super(ResidualBiGRU, self).__init__()\n",
    "\n",
    "        self.hidden_size = hidden_size\n",
    "        self.n_layers = n_layers\n",
    "\n",
    "        self.gru = nn.GRU(\n",
    "            hidden_size,\n",
    "            hidden_size,\n",
    "            n_layers,\n",
    "            batch_first=True,\n",
    "            bidirectional=bidir,\n",
    "        )\n",
    "        dir_factor = 2 if bidir else 1\n",
    "        self.fc1 = nn.Linear(\n",
    "            hidden_size * dir_factor, hidden_size * dir_factor * 2\n",
    "        )\n",
    "        self.ln1 = nn.LayerNorm(hidden_size * dir_factor * 2)\n",
    "        self.fc2 = nn.Linear(hidden_size * dir_factor * 2, hidden_size)\n",
    "        self.ln2 = nn.LayerNorm(hidden_size)\n",
    "\n",
    "    def forward(self, x, h=None):\n",
    "        res, new_h = self.gru(x, h)\n",
    "        # res.shape = (batch_size, sequence_size, 2*hidden_size)\n",
    "\n",
    "        res = self.fc1(res)\n",
    "        res = self.ln1(res)\n",
    "        res = nn.functional.relu(res)\n",
    "\n",
    "        res = self.fc2(res)\n",
    "        res = self.ln2(res)\n",
    "        res = nn.functional.relu(res)\n",
    "\n",
    "        # skip connection\n",
    "        res = res + x\n",
    "\n",
    "        return res, new_h\n",
    "\n",
    "class MultiResidualBiGRU(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, out_size, n_layers, bidir=True):\n",
    "        super(MultiResidualBiGRU, self).__init__()\n",
    "\n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.out_size = out_size\n",
    "        self.n_layers = n_layers\n",
    "\n",
    "        self.fc_in = nn.Linear(input_size, hidden_size)\n",
    "        self.ln = nn.LayerNorm(hidden_size)\n",
    "        self.res_bigrus = nn.ModuleList(\n",
    "            [\n",
    "                ResidualBiGRU(hidden_size, n_layers=1, bidir=bidir)\n",
    "                for _ in range(n_layers)\n",
    "            ]\n",
    "        )\n",
    "        self.fc_out = nn.Linear(hidden_size, out_size)\n",
    "\n",
    "    def forward(self, x, h=None):\n",
    "        # if we are at the beginning of a sequence (no hidden state)\n",
    "        if h is None:\n",
    "            # (re)initialize the hidden state\n",
    "            h = [None for _ in range(self.n_layers)]\n",
    "\n",
    "        x = self.fc_in(x)\n",
    "        x = self.ln(x)\n",
    "        x = nn.functional.relu(x)\n",
    "\n",
    "        new_h = []\n",
    "        for i, res_bigru in enumerate(self.res_bigrus):\n",
    "            x, new_hi = res_bigru(x, h[i])\n",
    "            new_h.append(new_hi)\n",
    "\n",
    "        x = self.fc_out(x)\n",
    "#         x = F.normalize(x,dim=0)\n",
    "        return x, new_h  # log probabilities + hidden states"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13645e66",
   "metadata": {
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     "duration": 0.002319,
     "end_time": "2023-10-14T02:44:00.064276",
     "exception": false,
     "start_time": "2023-10-14T02:44:00.061957",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Define Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01a5b6ec",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "SIGMA = 720 #average length of day is 24*60*12 = 17280 for comparison\n",
    "SAMPLE_FREQ = 12 # 1 obs per minute\n",
    "class SleepDataset(Dataset):\n",
    "    def __init__(\n",
    "        self,\n",
    "        file\n",
    "    ):\n",
    "        self.targets,self.data,self.ids = joblib.load(file)\n",
    "            \n",
    "    def downsample_seq_generate_features(self,feat, downsample_factor = SAMPLE_FREQ):\n",
    "        # downsample data and generate features\n",
    "        if len(feat)%SAMPLE_FREQ==0:\n",
    "            feat = np.concatenate([feat,np.zeros(SAMPLE_FREQ-((len(feat))%SAMPLE_FREQ))+feat[-1]])\n",
    "        feat = np.reshape(feat, (-1,SAMPLE_FREQ))\n",
    "        feat_mean = np.mean(feat,1)\n",
    "        feat_std = np.std(feat,1)\n",
    "        feat_median = np.median(feat,1)\n",
    "        feat_max = np.max(feat,1)\n",
    "        feat_min = np.min(feat,1)\n",
    "\n",
    "        return np.dstack([feat_mean,feat_std,feat_median,feat_max,feat_min])[0]\n",
    "    def downsample_seq(self,feat, downsample_factor = SAMPLE_FREQ):\n",
    "        # downsample data\n",
    "        if len(feat)%SAMPLE_FREQ==0:\n",
    "            feat = np.concatenate([feat,np.zeros(SAMPLE_FREQ-((len(feat))%SAMPLE_FREQ))+feat[-1]])\n",
    "        feat = np.reshape(feat, (-1,SAMPLE_FREQ))\n",
    "        feat_mean = np.mean(feat,1)\n",
    "        return feat_mean\n",
    "    \n",
    "    def gauss(self,n=SIGMA,sigma=SIGMA*0.15):\n",
    "        # guassian distribution function\n",
    "        r = range(-int(n/2),int(n/2)+1)\n",
    "        return [1 / (sigma * sqrt(2*pi)) * exp(-float(x)**2/(2*sigma**2)) for x in r]\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.targets)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        X = self.data[index][['anglez','enmo']]\n",
    "        y = self.targets[index]\n",
    "        \n",
    "        # turn target inds into array\n",
    "        target_guassian = np.zeros((len(X),2))\n",
    "        for s,e in y:\n",
    "            st1,st2 = max(0,s-SIGMA//2),s+SIGMA//2+1\n",
    "            ed1,ed2 = e-SIGMA//2,min(len(X),e+SIGMA//2+1)\n",
    "            target_guassian[st1:st2,0] = self.gauss()[st1-(s-SIGMA//2):]\n",
    "            target_guassian[ed1:ed2,1] = self.gauss()[:SIGMA+1-((e+SIGMA//2+1)-ed2)]\n",
    "            gc.collect()\n",
    "        y = target_guassian\n",
    "        gc.collect()\n",
    "        X = np.concatenate([self.downsample_seq_generate_features(X.values[:,i],SAMPLE_FREQ) for i in range(X.shape[1])],-1)\n",
    "        gc.collect()\n",
    "        y = np.dstack([self.downsample_seq(y[:,i],SAMPLE_FREQ) for i in range(y.shape[1])])[0]\n",
    "        gc.collect()\n",
    "        y = normalize(torch.from_numpy(y))\n",
    "        X = torch.from_numpy(X)\n",
    "        return X, y\n",
    "train_ds = SleepDataset('./train_data.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e19bfca0",
   "metadata": {
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     "start_time": "2023-10-14T02:44:08.578860",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Train and Eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "991f3f0c",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_size = int(TRAIN_PROP * len(train_ds))\n",
    "valid_size = len(train_ds) - train_size\n",
    "indices = torch.randperm(len(train_ds))\n",
    "train_sampler = SubsetRandomSampler(indices[:train_size])\n",
    "valid_sampler = SubsetRandomSampler(\n",
    "    indices[train_size : train_size + valid_size]\n",
    ")\n",
    "steps = train_size*EPOCHS\n",
    "warmup_steps = int(steps*WARMUP_PROP)\n",
    "model = MultiResidualBiGRU(input_size=10,hidden_size=64,out_size=2,n_layers=5).to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3,weight_decay = 0)\n",
    "scheduler = CosineLRScheduler(optimizer,t_initial= steps,warmup_t=warmup_steps, warmup_lr_init=1e-6,lr_min=2e-8,)\n",
    "dt = time.time()\n",
    "model_path = './'\n",
    "history = {\n",
    "    \"train_loss\": [],\n",
    "    \"valid_loss\": [],\n",
    "    \"valid_mAP\": [],\n",
    "    \"lr\": [],\n",
    "}\n",
    "best_valid_loss = np.inf\n",
    "criterion = torch.nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1df2670c",
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    "_kg_hide-input": true,
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   "source": [
    "def evaluate(\n",
    "    model: nn.Module, max_chunk_size: int, loader: DataLoader, device, criterion\n",
    "):\n",
    "    model.eval()\n",
    "    valid_loss = 0.0\n",
    "    y_true_full = torch.FloatTensor([]).half()\n",
    "    y_pred_full = torch.FloatTensor([]).half()\n",
    "    for X_batch, y_batch in tqdm(loader, desc=\"Eval\", unit=\"batch\"):\n",
    "        # as some of the sequences we are dealing with are pretty long, we\n",
    "        # use a chunk-based approach\n",
    "        y_batch = y_batch.to(device, non_blocking=True)\n",
    "        pred = torch.zeros(y_batch.shape).to(device, non_blocking=True).half()\n",
    "\n",
    "        # (re)initialize model's hidden state(s)\n",
    "        h = None\n",
    "\n",
    "        # number of chunks for this sequence (we assume batch size = 1)\n",
    "        seq_len = X_batch.shape[1]\n",
    "        for i in range(0, seq_len, max_chunk_size):\n",
    "            X_chunk = X_batch[:, i : i + max_chunk_size].float().to(device, non_blocking=True)\n",
    "\n",
    "            y_pred, h = model(X_chunk, h)\n",
    "            h = [hi.detach() for hi in h]\n",
    "            pred[:, i : i + max_chunk_size] = y_pred.half()\n",
    "            del X_chunk\n",
    "            gc.collect()\n",
    "        loss = criterion(\n",
    "            pred.float(),\n",
    "            y_batch.float(),\n",
    "        )\n",
    "        valid_loss += loss.item()\n",
    "        del pred,loss\n",
    "        gc.collect()\n",
    "\n",
    "    valid_loss /= len(loader)\n",
    "\n",
    "    y_true_full = y_true_full.squeeze(0)\n",
    "    y_pred_full = y_pred_full.squeeze(0)\n",
    "    gc.collect()\n",
    "    return valid_loss"
   ]
  },
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     "start_time": "2023-10-14T02:44:14.234592",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4cd211106804d98a560721094f13123",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training:   0%|          | 0/249 [00:01<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "======= Backtrace: =========\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b3aa8b5889a4928b28f6844f63b3cde",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Eval:   0%|          | 0/28 [00:01<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "======= Backtrace: =========\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "/lib64/libc.so.6(+0x7340f)[0x7fdd2cf3640f]\n",
      "/lib64/libc.so.6(+0x78c7e)[0x7fdd2cf3bc7e]\n",
      "/lib64/ld-linux-x86-64.so.2(_dl_deallocate_tls+0x39)[0x7fdd2dba7589]\n",
      "/lib64/libpthread.so.0(+0x7237)[0x7fdd2d97f237]\n",
      "/lib64/libpthread.so.0(+0x734f)[0x7fdd2d97f34f]\n",
      "/lib64/libpthread.so.0(+0x82f8)[0x7fdd2d9802f8]\n",
      "/lib64/libc.so.6(clone+0x6d)[0x7fdd2cfa9fad]\n",
      "======= Memory map: ========\n",
      "00400000-0089d000 r-xp 00000000 08:03 36090448                           /home/wangcx/python3.12/bin/python3.12\n",
      "00a9c000-00a9d000 r--p 0049c000 08:03 36090448                           /home/wangcx/python3.12/bin/python3.12\n",
      "00a9d000-00c0b000 rw-p 0049d000 08:03 36090448                           /home/wangcx/python3.12/bin/python3.12\n",
      "00c0b000-00c0d000 rw-p 00000000 00:00 0 \n",
      "01e66000-d2705000 rw-p 00000000 00:00 0                                  [heap]\n",
      "d2705000-d2d6d000 rw-p 00000000 00:00 0                                  [heap]\n",
      "200000000-200200000 ---p 00000000 00:00 0 \n",
      "200200000-200400000 rw-s 00000000 00:05 22926                            /dev/nvidiactl\n",
      "200400000-200600000 rw-s 00000000 00:05 19726                            /dev/nvidia0\n",
      "200600000-203e00000 rw-s 00000000 00:05 22926                            /dev/nvidiactl\n",
      "203e00000-204c00000 ---p 00000000 00:00 0 \n",
      "204c00000-204e00000 rw-s 00000000 00:05 22926                            /dev/nvidiactl\n",
      "204e00000-205000000 rw-s 00000000 00:05 22926                            /dev/nvidiactl\n",
      "205200000-205400000 rw-s 00000000 00:05 22926                            /dev/nvidiactl\n",
      "205400000-205600000 ---p 00000000 00:00 0 \n",
      "205600000-205800000 rw-s 00000000 00:05 22926                            /dev/nvidiactl\n",
      "205800000-205a00000 ---p 00000000 00:00 0 \n",
      "205a00000-205c00000 rw-s 00000000 00:04 7146986                          /dev/zero (deleted)\n",
      "205c00000-600200000 ---p 00000000 00:00 0 \n",
      "10000000000-10004000000 ---p 00000000 00:00 0 \n",
      "7fd6d0000000-7fd6d0021000 rw-p 00000000 00:00 0 \n",
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      "7fd6d6000000-7fd757200000 ---p 00000000 00:00 0 \n",
      "7fd757200000-7fd757a00000 rw-s 00000000 00:04 9138                       /dev/zero (deleted)\n",
      "7fd757a00000-7fd758000000 ---p 00000000 00:00 0 \n",
      "7fd758000000-7fd758026000 rw-p 00000000 00:00 0 \n",
      "7fd758026000-7fd75c000000 ---p 00000000 00:00 0 \n",
      "7fd75c000000-7fd75c026000 rw-p 00000000 00:00 0 \n",
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      "7fd760000000-7fd760026000 rw-p 00000000 00:00 0 \n",
      "7fd760026000-7fd764000000 ---p 00000000 00:00 0 \n",
      "7fd764000000-7fd764026000 rw-p 00000000 00:00 0 \n",
      "7fd764026000-7fd768000000 ---p 00000000 00:00 0 \n",
      "7fd768000000-7fd775400000 ---p 00000000 00:00 0 \n",
      "7fd775400000-7fd775600000 rw-s 00000000 00:04 7147062                    /dev/zero (deleted)\n",
      "7fd775600000-7fd775a00000 rw-s 00000000 00:04 9002                       /dev/zero (deleted)\n",
      "7fd775a00000-7fd775e00000 rw-s 00000000 00:04 9005                       /dev/zero (deleted)\n",
      "7fd775e00000-7fd776000000 rw-s 00000000 00:04 9006                       /dev/zero (deleted)\n",
      "7fd776000000-7fd783200000 ---p 00000000 00:00 0 \n",
      "7fd783200000-7fd783a00000 rw-s 00000000 00:04 9009                       /dev/zero (deleted)\n",
      "7fd783a00000-7fd783e00000 rw-s 00000000 00:04 9021                       /dev/zero (deleted)\n",
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      "7fd791000000-7fd791800000 rw-s 00000000 00:04 9018                       /dev/zero (deleted)\n",
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      "7fd792000000-7fd7ad000000 ---p 00000000 00:00 0 \n",
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      "7fd7ae000000-7fd7c8000000 ---p 00000000 00:00 0 \n",
      "7fd7c8000000-7fd7c8026000 rw-p 00000000 00:00 0 \n",
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      "7fd7dc000000-7fd7dc026000 rw-p 00000000 00:00 0 \n",
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      "7fd7e4000000-7fd7e4026000 rw-p 00000000 00:00 0 \n",
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      "7fd7ec000000-7fd7ec026000 rw-p 00000000 00:00 0 \n",
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      "7fd7f0000000-7fd7f0026000 rw-p 00000000 00:00 0 \n",
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      "7fd7f4000000-7fd7f4026000 rw-p 00000000 00:00 0 \n",
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      "7fd7f8000000-7fd7f9af2000 rw-p 00000000 00:00 0 \n",
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      "7fd82fc00000-7fd82fe00000 rw-s 00000000 00:04 7147044                    /dev/zero (deleted)\n",
      "7fd82fe00000-7fd830000000 rw-s 00000000 00:04 7147051                    /dev/zero (deleted)\n",
      "7fd830000000-7fd83c200000 ---p 00000000 00:00 0 \n",
      "7fd83c200000-7fd83ca00000 rw-s 00000000 00:04 7147043                    /dev/zero (deleted)\n",
      "7fd83ca00000-7fd83ce00000 rw-s 00000000 00:04 7147047                    /dev/zero (deleted)\n",
      "7fd83ce00000-7fd83d200000 rw-s 00000000 00:04 7147050                    /dev/zero (deleted)\n",
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      "7fd8628a8000-7fd866c3f000 r-xp 00000000 08:03 36109287                   /home/wangcx/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/libcudnn_ops_train.so.8\n",
      "7fd866c3f000-7fd866e3f000 ---p 04397000 08:03 36109287                   /home/wangcx/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/libcudnn_ops_train.so.8\n",
      "7fd866e3f000-7fd866e44000 r--p 04397000 08:03 36109287                   /home/wangcx/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/libcudnn_ops_train.so.8\n",
      "7fd866e44000-7fd866e45000 rw-p 0439c000 08:03 36109287                   /home/wangcx/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/libcudnn_ops_train.so.8\n",
      "7fd866e45000-7fd866e7c000 rw-p 00000000 00:00 0 \n",
      "7fd866e7c000-7fd866e82000 rw-p 0439e000 08:03 36109287                   /home/wangcx/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/libcudnn_ops_train.so.8\n",
      "7fd866e82000-7fd86dd4d000 r-xp 00000000 08:03 36109283                   /home/wangcx/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/libcudnn_adv_train.so.8\n",
      "7fd86dd4d000-7fd86df4d000 ---p 06ecb000 08:03 36109283                   /home/wangcx/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/libcudnn_adv_train.so.8\n",
      "7fd86df4d000-7fd86df53000 r--p 06ecb000 08:03 36109283                   /home/wangcx/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/libcudnn_adv_train.so.8\n",
      "7fd86df53000-7fd86df54000 rw-p 06ed1000 08:03 36109283                   /home/wangcx/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/libcudnn_adv_train.so.8\n",
      "7fd86df54000-7fd86dff6000 rw-p 00000000 00:00 0 \n",
      "7fd86dff6000-7fd86e000000 rw-p 06ed3000 08:03 36109283                   /home/wangcx/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib/libcudnn_adv_train.so.8\n",
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      "7fd888000000-7fd88bfff000 rw-p 00000000 00:00 0 \n",
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      "7fd898026000-7fd89c000000 ---p 00000000 00:00 0 \n",
      "7fd89c======= Backtrace: =========\n",
      "000000-7fd89c026000 rw-p 00000000 00:00 0 \n",
      "7fd89c026000-7fd8a0000000 ---p 00000000 00:00 0 \n",
      "7fd8a0000000-7fd8a0026000 rw-p 00000000 00:00 0 \n",
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      "7fd8b87fe000-7fd8b8ffe000 rw-p 00000000 00:00 0 \n",
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      "7fd8b97ff000-7fd8b9800000 ---p 00000000 00:00 0 \n",
      "7fd8b9800000-7fd8ba000000 rw-p 00000000 00:00 0 \n",
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      "7fd8bb600000-7fd8bb800000 rw-s 00000000 00:04 7147037                    /dev/zero (deleted)\n",
      "7fd8bb800000-7fd8bbc00000 rw-s 00000000 00:04 7147040                    /dev/zero (deleted)\n",
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      "7fd8c8001000-7fd8ca000000 rw-p 00000000 00:00 0 \n",
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      "7fd8cb000000-7fd8cb400000 rw-s 00000000 00:04 7147029                    /dev/zero (deleted)\n",
      "7fd8cb400000-7fd8cb600000 rw-s 00000000 00:04 7147030                    /dev/zero (deleted)\n",
      "7fd8cb600000-7fd8cc000000 ---p 00000000 00:00 0 \n",
      "7fd8cc000000-7fd8cc026000 rw-p 00000000 00:00 0 \n",
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      "7fd8d1fe4000-7fd8d1fe5000 ---p 00000000 00:00 0 \n",
      "7fd8d1fe5000-7fd8d27e5000 rw-p 00000000 00:00 0 \n",
      "7fd8d27e5000-7fd8d27e6000 ---p 00000000 00:00 0 \n",
      "7fd8d27e6000-7fd8d2fe6000 rw-p 00000000 00:00 0 \n",
      "7fd8d2fe6000-7fd8d2fe7000 ---p 00000000 00:00 0 \n",
      "7fd8d2fe7000-7fd8d37e7000 rw-p 00000000 00:00 0 \n",
      "7fd8d37e7000-7fd8d37e8000 ---p 00000000 00:00 0 \n",
      "7fd8d37e8000-7fd8d3fe8000 rw-p 00000000 00:00 0 \n",
      "7fd8d3fe8000-7fd8d3fe9000 ---p 00000000 00:00 0 \n",
      "7fd8d3fe9000-7fd8d47e9000 rw-p 00000000 00:00 0 \n",
      "7fd8d47e9000-7fd8d47ea000 ---p 00000000 00:00 0 \n",
      "7fd8d47ea000-7fd8d4fea000 rw-p 00000000 00:00 0 \n",
      "7fd8d4fea000-7fd8d4feb000 ---p 00000000 00:00 0 \n",
      "7fd8d4feb000-7fd8d57eb000 rw-p 00000000 00:00 0 \n",
      "7fd8d57eb000-7fd8d57ec000 ---p 00000000 00:00 0 \n",
      "7fd8d57ec000-7fd8d5fec000 rw-p 00000000 00:00 0 \n",
      "7fd8d5fec000-7fd8d5fed000 ---p 00000000 00:00 0 \n",
      "7fd8d5fed000-7fd8d67ed000 rw-p 00000000 00:00 0 \n",
      "7fd8d67ed000-7fd8d67ee000 ---p 00000000 00:00 0 \n",
      "7fd8d67ee000-7fd8d6fee000 rw-p 00000000 00:00 0 \n",
      "7fd8d6fee000-7fd8d6fef000 ---p 00000000 00:00 0 \n",
      "7fd8d6fef000-7fd8d77ef000 rw-p 00000000 00:00 0 \n",
      "7fd8d77ef000-7fd8d77f0000 ---p 00000000 00:00 0 \n",
      "7fd8d77f0000-7fd8d7ff0000 rw-p 00000000 00:00 0 \n",
      "7fd8d7ff0000-7fd8d7ff1000 ---p 00000000 00:00 0 \n",
      "7fd8d7ff1000-7fd8d87f1000 rw-p 00000000 00:00 0 \n",
      "7fd8d87f1000-7fd8d87f2000 ---p 00000000 00:00 0 \n",
      "7fd8d87f2000-7fd8d8ff/lib64/libc.so.6(+0x7340f)[0x7fdd2cf3640f]\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "2000 rw-p 00000000 00:00 0 \n",
      "7fd8d8ff2000-7fd8d8ff3000 ---p 00000000 00:00 0 \n",
      "7fd8d8ff3000-7fd8d97f3000 rw-p 00000000 00:00 0 \n",
      "7fd8d97f3000-7fd8d97f4000 ---p 00000000 00:00 0 \n",
      "7fd8d97f4000-7fd8d9ff4000 rw-p 00000000 00:00 0 \n",
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      "7fd8d9ff5000-7fd8da7f5000 rw-p 00000000 00:00 0 \n",
      "7fd8da7f5000-7fd8da7f6000 ---p 00000000 00:00 0 \n",
      "7fd8da7f6000-7fd8daff6000 rw-p 00000000 00:00 0 \n",
      "7fd8daff6000-7fd8daff7000 ---p 00000000 00:00 0 \n",
      "7fd8daff7000-7fd8db7f7000 rw-p 00000000 00:00 0 \n",
      "7fd8db7f7000-7fd8db7f8000 ---p 00000000 00:00 0 \n",
      "7fd8db7f8000-7fd8dbff8000 rw-p 00000000 00:00 0 \n",
      "7fd8dbff8000-7fd8dbff9000 ---p 00000000 00:00 0 \n",
      "7fd8dbff9000-7fd8dc7f9000 rw-p 00000000 00:00 0 \n",
      "7fd8dc7f9000-7fd8dc7fa000 ---p 00000000 00:00 0 \n",
      "7fd8dc7fa000-7fd8dcffa000 rw-p 00000000 00:00 0 \n",
      "7fd8dcffa000-7fd8dcffb000 ---p 00000000 00:00 0 \n",
      "7fd8dcffb000-7fd8dd7fb000 rw-p 00000000 00:00 0 \n",
      "7fd8dd7fb000-7fd8dd7fc000 ---p 00000000 00:00 0 \n",
      "7fd8dd7fc000-7fd8ddffc000 rw-p 00000000 00:00 0 \n",
      "7fd8ddffc000-7fd8ddffd000 ---p 00000000 00:00 0 \n",
      "7fd8ddffd000-7fd8de7fd000 rw-p 00000000 00:00 0 \n",
      "7fd8de7fd000-7fd8de7fe000 ---p 00000000 00:00 0 \n",
      "7fd8de7fe000-7fd8deffe000 rw-p 00000000 00:00 0 \n",
      "7fd8deffe000-7fd8defff000 ---p 00000000 00:00 0 \n",
      "7fd8defff000-7fd8df7ff000 rw-p 00000000 00:00 0 \n",
      "7fd8df7ff000-7fd8df800000 ---p 00000000 00:00 0 \n",
      "7fd8df800000-7fd8e0000000 rw-p 00000000 00:00 0 \n",
      "7fd8e0000000-7fd8e0800000 rw-s 00000000 00:04 7147022                    /dev/zero (deleted)\n",
      "7fd8e0800000-7fd8e0a00000 rw-s 00000000 00:04 7147023                    /dev/zero (deleted)\n",
      "7fd8e0a00000-7fd8e2000000 ---p 00000000 00:00 0 \n",
      "7fd8e2000000-7fd8e2800000 rw-s 00000000 00:04 4551437                    /dev/zero (deleted)\n",
      "7fd8e2800000-7fd8e2a00000 rw-s 00000000 00:04 4551438                    /dev/zero (deleted)\n",
      "7fd8e2a00000-7fd8e2e00000 rw-s 00000000 00:04 4551441                    /dev/zero (deleted)\n",
      "7fd8e2e00000-7fd8e3200000 rw-s 00000000 00:04 7147015                    /dev/zero (deleted)\n",
      "7fd8e3200000-7fd8e3400000 rw-s 00000000 00:04 7147016                    /dev/zero (deleted)\n",
      "7fd8e3400000-7fd8e3800000 rw-s 00000000 00:04 7147019                    /dev/zero (deleted)\n",
      "7fd8e3800000-7fd8e4000000 ---p 00000000 00:00 0 \n",
      "7fd8e4000000-7fd8e4400000 rw-s 00000000 00:04 10622332                   /dev/zero (deleted)\n",
      "7fd8e4400000-7fd8e4800000 rw-s 00000000 00:04 10622335                   /dev/zero (deleted)\n",
      "7fd8e4800000-7fd8e4c00000 rw-s 00000000 00:04 7147001                    /dev/zero (deleted)\n",
      "7fd8e4c00000-7fd8e4e00000 rw-s 00000000 00:04 7147002                    /dev/zero (deleted)\n",
      "7fd8e4e00000-7fd8e5200000 rw-s 00000000 00:04 7147005                    /dev/zero (deleted)\n",
      "7fd8e5200000-7fd8e5600000 rw-s 00000000 00:04 7147008                    /dev/zero (deleted)\n",
      "7fd8e5600000-7fd8e5800000 rw-s 00000000 00:04 7147009                    /dev/zero (deleted)\n",
      "7fd8e5800000-7fd8e5c00000 rw-s 00000000 00:04 7147012                    /dev/zero (deleted)\n",
      "7fd8e5c00000-7fd8e6000000 ---p 00000000 00:00 0 \n",
      "7fd8e6000000-7fd8e6400000 rw-s 000000/lib64/libc.so.6(+0x78c7e)[0x7fdd2cf3bc7e]\n",
      "00 00:04 10622318                   /dev/zero (deleted)\n",
      "7fd8e6400000-7fd8e6800000 rw-s 00000000 00:04 10800355                   /dev/zero (deleted)\n",
      "7fd8e6800000-7fd8e6a00000 rw-s 00000000 00:04 4551431                    /dev/zero (deleted)\n",
      "7fd8e6a00000-7fd8e7200000 rw-s 00000000 00:04 4551434                    /dev/zero (deleted)\n",
      "7fd8e7200000-7fd8e7600000 rw-s 00000000 00:04 10622321                   /dev/zero (deleted)\n",
      "7fd8e7600000-7fd8e7800000 rw-s 00000000 00:04 10622322                   /dev/zero (deleted)\n",
      "7fd8e7800000-7fd8e7c00000 rw-s 00000000 00:04 10622325                   /dev/zero (deleted)\n",
      "7fd8e7c00000-7fd8e8000000 rw-s 00000000 00:04 10622328                   /dev/zero (deleted)\n",
      "7fd8e8000000-7fd8e8800000 rw-s 00000000 00:04 10800351                   /dev/zero (deleted)\n",
      "7fd8e8800000-7fd8e8c00000 rw-s 00000000 00:04 10622301                   /dev/zero (deleted)\n",
      "7fd8e8c00000-7fd8e9000000 rw-s 00000000 00:04 10622304                   /dev/zero (deleted)\n",
      "7fd8e9000000-7fd8e9200000 rw-s 0000000/lib64/ld-linux-x86-64.so.2(_dl_deallocate_tls+0x39)[0x7fdd2dba7589]\n",
      "/lib64/libpthread.so.0(+0x7237)[0x7fdd2d97f237]\n",
      "0 00:04 10622305                   /dev/zero (deleted)\n",
      "7fd8e9200000-7fd8e9600000 rw-s 00000000 00:04 10622308                   /dev/zero (deleted)\n",
      "7fd8e9600000-7fd8e9a00000 rw-s 00000000 00:04 10622311                   /dev/zero (deleted)\n",
      "7fd8e9a00000-7fd8e9e00000 rw-s 00000000 00:04 10622314                   /dev/zero (deleted)\n",
      "7fd8e9e00000-7fd8ea000000 rw-s 00000000 00:04 10622315                   /dev/zero (deleted)\n",
      "7fd8ea000000-7fd8ea400000 rw-s 00000000 00:04 10805288                   /dev/zero (deleted)\n",
      "7fd8ea400000-7fd8ea600000 rw-s 00000000 00:04 10805289                   /dev/zero (deleted)\n",
      "7fd8ea600000-7fd8eaa00000 rw-s 00000000 00:04 10805292                   /dev/zero (deleted)\n",
      "7fd8eaa00000-7fd8eae00000 rw-s 00000000 00:04 10805295                   /dev/zero (deleted)\n",
      "7fd8eae00000-7fd8eb000000 rw-s 00000000 00:04 10805296                   /dev/zero (deleted)\n",
      "7fd8eb000000-7fd8eb800000 rw-s 00000000 00:04 10805299                   /dev/zero (deleted)\n",
      "7fd8eb800000-7fd8ebc00000 rw-s 00000000/lib64/libpthread.so.0(+0x734f)[0x7fdd2d97f34f]\n",
      "/lib64/libpthread.so.0(+0x82f8)[0x7fdd2d9802f8]\n",
      " 00:04 10805302                   /dev/zero (deleted)\n",
      "7fd8ebc00000-7fd8ebe00000 rw-s 00000000 00:04 10800346                   /dev/zero (deleted)\n",
      "7fd8ebe00000-7fd8ec000000 rw-s 00000000 00:04 10622329                   /dev/zero (deleted)\n",
      "7fd8ec000000-7fd8ec800000 rw-s 00000000 00:04 10800341                   /dev/zero (deleted)\n",
      "7fd8ec800000-7fd8eca00000 rw-s 00000000 00:04 10800342                   /dev/zero (deleted)\n",
      "7fd8eca00000-7fd8ece00000 rw-s 00000000 00:04 10805274                   /dev/zero (deleted)\n",
      "7fd8ece00000-7fd8ed200000 rw-s 00000000 00:04 10805278                   /dev/zero (deleted)\n",
      "7fd8ed200000-7fd8ed600000 rw-s 00000000 00:04 10805281                   /dev/zero (deleted)\n",
      "7fd8ed600000-7fd8ed800000 rw-s 00000000 00:04 10805282                   /dev/zero (deleted)\n",
      "7fd8ed800000-7fd8ee000000 rw-s 00000000 00:04 10805285                   /dev/zero (deleted)\n",
      "7fd8ee000000-7fd8ee400000 rw-s 00000000 00:04 10622234                   /dev/zero (deleted)\n",
      "7fd8ee400000-7fd8ee800000 rw-s 00000000 00:04 10622239                   /dev/zero (deleted)\n",
      "7fd8ee800000-7fd8eea00000 rw-s 00000000 00:04 10622240                   /dev/zero (deleted)\n",
      "7fd8eea00000-7fd8eee00000 rw-s 00000000 00:04 10802900                   /dev/zero (deleted)\n",
      "7fd8eee00000-7fd8ef200000 rw-s 00000000 00:04 10789443                   /dev/zero (deleted)\n",
      "7fd8ef200000-7fd8ef400000 rw-s 00000000 00:04 10789444                   /dev/zero (deleted)\n",
      "7fd8ef400000-7fd8efc00000 rw-s 00000000 00:04 10360642                   /dev/zero (deleted)\n",
      "7fd8efc00000-7fd8f0000000 rw-s 00000000 00:04 10800337                   /dev/zero (deleted)\n",
      "7fd8f0000000-7fd8f0400000 rw-s 00000000 00:04 5421156                    /dev/zero (deleted)\n",
      "7fd8f0400000-7fd8f0c00000 rw-s 00000000 00:04 5421159                    /dev/zero (deleted)\n",
      "7fd8f0c00000-7fd8f0e00000 rw-s 00000000 00:04 5421160                    /dev/zero (deleted)\n",
      "7fd8f0e00000-7fd8f1200000 rw-s 00000000 00:04 10318779                   /dev/zero (deleted)\n",
      "7fd8f1200000-7fd8f1600000 rw-s 00000000 00:04 10318784                   /dev/zero (deleted)\n",
      "7fd8f1600000-7fd8f1800000 rw-s 00000000 00:04 7341851                    /dev/zero (deleted)\n",
      "7fd8f1800000-7fd8f1c00000 rw-s 00000000 00:04 10789440                   /dev/zero (deleted)\n",
      "7fd8f1c00000-7fd8f2000000 rw-s 00000000 00:04 10622230                   /dev/zero (deleted)\n",
      "7fd8f2000000-7fd8f2400000 rw-s 00000000 00:04 10800291                   /dev/zero (deleted)\n",
      "7fd8f2400000-7fd8f2600000 rw-s 00000000 00:04 10800292                   /dev/zero (deleted)\n",
      "7fd8f2600000-7fd8f2a00000 rw-s 00000000 00:04 5445603                    /dev/zero (deleted)\n",
      "7fd8f2a00000-7fd8f2e00000 rw-s 00000000 00:04 5445606                    /dev/zero (deleted)\n",
      "7fd8f2e00000-7fd8f3000000 rw-s 00000000 00:04 5445607                    /dev/zero (deleted)\n",
      "7fd8f3000000-7fd8f3400000 rw-s 00000000 00:04 5445610                    /dev/zero (deleted)\n",
      "7fd8f3400000-7fd8f3600000 rw-s 00000000 00:04 10318772                   /dev/zero (deleted)\n",
      "7fd8f3600000-7fd8f3e00000 rw-s 00000000 00/lib64/libc.so.6(clone+0x6d)[0x7fdd2cfa9fad]\n",
      "======= Memory map: ========\n",
      ":04 5421154                    /dev/zero (deleted)\n",
      "7fd8f3e00000-7fd8f4000000 rw-s 00000000 00:04 5445611                    /dev/zero (deleted)\n",
      "7fd8f4000000-7fd8f4400000 rw-s 00000000 00:04 5445575                    /dev/zero (deleted)\n",
      "7fd8f4400000-7fd8f4600000 rw-s 00000000 00:04 5445578                    /dev/zero (deleted)\n",
      "7fd8f4600000-7fd8f4a00000 rw-s 00000000 00:04 5445581                    /dev/zero (deleted)\n",
      "7fd8f4a00000-7fd8f4c00000 rw-s 00000000 00:04 5445582                    /dev/zero (deleted)\n",
      "7fd8f4c00000-7fd8f5000000 rw-s 00000000 00:04 10800284                   /dev/zero (deleted)\n",
      "7fd8f5000000-7fd8f5400000 rw-s 00000000 00:04 4551430                    /dev/zero (deleted)\n",
      "7fd8f5400000-7fd8f5800000 rw-s 00000000 00:04 10800287                   /dev/zero (deleted)\n",
      "7fd8f5800000-7fd8f5a00000 rw-s 00000000 00:04 10800288                   /dev/zero (deleted)\n",
      "7fd8f5a00000-7fd8f5e00000 rw-s 00000000 00:04 5445602                    /dev/zero (deleted)\n",
      "7fd8f5e00000-7fd8f6000000 rw-s 00000000 00:04 10622231                   /dev/zero (deleted)\n",
      "7fd8f6000000-7fd8f6800000 rw-s 00000000 00:04 10800276                   /dev/zero (deleted)\n",
      "7fd8f6800000-7fd8f6a00000 rw-s 00000000 00:04 10800277                   /dev/zero (deleted)\n",
      "7fd8f6a00000-7fd8f6e00000 rw-s 00000000 00:04 10800280                   /dev/zero (deleted)\n",
      "7fd8f6e00000-7fd8f7600000 rw-s 00000000 00:04 5445568                    /dev/zero (deleted)\n",
      "7fd8f7600000-7fd8f7800000 rw-s 00000000 00:04 5445569                    /dev/zero (deleted)\n",
      "7fd8f7800000-7fd8f7c00000 rw-s 00000000 00:04 10318761                   /dev/zero (deleted)\n",
      "7fd8f7c00000-7fd8f7e00000 rw-s 00000000 00:04 5445572                    /dev/zero (deleted)\n",
      "7fd8f7e00000-7fd8f8000000 rw-s 00000000 00:04 10800338                   /dev/zero (deleted)\n",
      "7fd8f8000000-7fd8f8021000 rw-p 00000000 00:00 0 \n",
      "7fd8f8021000-7fd8fc000000 ---p 00000000 00:00 0 \n",
      "7fd8fc000000-7fd8fc021000 rw-p 00000000 00:00 0 \n",
      "7fd8fc021000-7fd900000000 ---p 00000000 00:00 0 \n",
      "7fd900000000-7fd900021000 rw-p 000*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "00000 00:00 0 \n",
      "7fd900021000-7fd904000000 ---p 00000000 00:00 0 \n",
      "7fd904000000-7fd904021000 rw-p 00000000 00:00 0 \n",
      "7fd904021000-7fd908000000 ---p 00000000 00:00 0 \n",
      "7fd908000000-7fd908021000 rw-p 00000000 00:00 0 \n",
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      "7fd90c000000-7fd90c021000 rw-p 00000000 00:00 0 \n",
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      "7fd910000000-7fd910021000 rw-p 00000000 00:00 0 \n",
      "7fd910021000-7fd914000000 ---p 00000000 00:00 0 \n",
      "7fd914110000-7fd9147f9000 rw-p 00000000 00:00 0 \n",
      "7fd918000000-7fd918021000 rw-p 00000000 00:00 0 \n",
      "7fd918021000-7fd91c000000 ---p 00000000 00:00 0 \n",
      "7fd91c000000-7fd91c021000 rw-p 00000000 00:00 0 \n",
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      "7fd920000000-7fd920021000 rw-p 00000000 00:00 0 \n",
      "7fd920021000-7fd924000000 ---p 00000000 00:00 0 \n",
      "7fd924000000-7fd924021000 rw-p 00000000 00:00 0 \n",
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      "7fd928000000-7fd928021000 rw-p 00000000 00:00 0 \n",
      "7fd928021000-7fd92c000000 ---p 00000000 00:00 0 \n",
      "7fd92c000000-7fd92c021000 rw-00400000-0089d000 r-xp 00000000 08:03 36090448                           /home/wangcx/python3.12/bin/python3.12\n",
      "00a9c000-00a9d000 r--p 0049c000 08:03 36090448                           /home/wangcx/python3.12/bin/python3.12\n",
      "00a9d000-00c0b000 rw-p 0049d000 08:03 36090448                           /home/wangcx/python3.12/bin/python3.12\n",
      "00c0b000-00c0d000 rw-p 00000000 00:00 0 \n",
      "01e66000-d2705000 rw-p 00000000 00:00 0                                  [heap]\n",
      "d2705000-d2ef8000 rw-p 00000000 00:00 0                                  [heap]\n",
      "200000000-200200000 ---p 00000000 00:00 0 \n",
      "200200000-200400000 rw-s 00000000 00:05 22926                            /dev/nvidiactl\n",
      "200400000-200600000 rw-s 00000000 00:05 19726                            /dev/nvidia0\n",
      "200600000-203e00000 rw-s 00000000 00:05 22926                            /dev/nvidiactl\n",
      "203e00000-204c00000 ---p 00000000 00:00 0 \n",
      "204c00000-204e00000 rw-s 00000000 00:05 22926                            /dev/nvidiactl\n",
      "204e00000-205000000 rw-s 00000000 00:05 22926      p 00000000 00:00 0 \n",
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      "7fd9357fc000-7fd935ffc000 rw-p 00000000 00:00 0 \n",
      "7fd938000000-7fd938021000 rw-p 00000000 00:00 0 \n",
      "7fd938021000-7fd93c000000 ---p 00000000 00:00 0 \n",
      "7fd93c000000-7fd93c021000 rw-p 00000000 00:00 0 \n",
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      "7fd940000000-7fd940021000 rw-p 00000000 00:00 0 \n",
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      "7fd944000000-7fd944021000 rw-p 00000000 00:00 0 \n",
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      "7fd948000000-7fd948021000 rw-p 00000000 00:00 0 \n",
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      "7fd94c000000-7fd94c021000 rw-p 00000000 00:00 0 \n",
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      "7fd954267000-7fd9547f9000 rw-p 00000000 00:00 0 \n",
      "7fd958000000-7fd958021000 rw-p 00000000 00:00 0 \n",
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      "7fd975ffd000-7fd976                      /dev/nvidiactl\n",
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      "205600000-205800000 rw-s 00000000 00:05 22926                            /dev/nvidiactl\n",
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      "7fd775a00000-7fd775e00000 rw-s 00000000 00:04 9005                       /dev/zero (deleted)\n",
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      "7fd9c2c00000-7fd9c2e00000 rw-s 00000000 00:04 10800147                   /dev/zero (deleted)\n",
      "7fd9c2e00000-7fd9c3200000 rw-s 00000000 00:04 10800150                   /dev/zero (deleted)\n",
      "7fd9c3200000-7fd9c3600000 rw-s 00000000 00:04 10800153                   /dev/zero (deleted)\n",
      "7fd9c3600000-7fd9c3800000 rw-s 00000000 00:04 10800154                   /dev/zero (deleted)\n",
      "7fd9c3800000-7fd9c3c00000 rw-s 00000000 00:04 10800157                   /dev/zero (deleted)\n",
      "7fd9c3c00000-7fd9c4000000 rw-s 00000000 00:04 10800181                   /dev/zero (deleted)\n",
      "7fd9c4000000-7fd9c4400000 rw-s 00000000 00:04 4551410                    /dev/zero (deleted)\n",
      "7fd9c4400000-7fd9c4800000 rw-s 00000000 00:04 4551413                    /dev/zero (deleted)\n",
      "7fd9c4800000-7fd9c4a00000 rw-s 00000000 00:04 4551414                    /dev/zero (deleted)\n",
      "7fd9c4a00000-7fd9c4e00000 rw-s 00000000 00:04 4551417                    /dev/zero (deleted)\n",
      "7fd9c4e00000-7fd9c5200000 rw-s 00000000 00:04 4551420                    /dev/zero (deleted)\n",
      "7fd9c5200000-7fd9c5600000 rw-s 00000000 00:04 4551423                    /dev/zero (deleted)\n",
      "7fd9c5600000-7fd9c5800000 rw-s 00000000 00:04 10800130          *** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/5 --  train_loss = 0.753794 --  valid_loss = 0.461757 --  time = 599.094795s\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "924244cdc5da454eaeb8499e5eaf6327",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training:   0%|          | 0/249 [00:01<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "======= Backtrace: =========\n",
      "/lib64/libc.so.6(+0x7340f)[0x7fdd2cf3640f]\n",
      "/lib64/libc.so.6(+0x78c7e)[0x7fdd2cf3bc7e]\n",
      "/lib64/ld-linux-x86-64.so.2(_dl_deallocate_tls+0x39)[0x7fdd2dba7589]\n",
      "/lib64/libpthread.so.0(+0x7237)[0x7fdd2d97f237]\n",
      "/lib64/libpthread.so.0(+0x734f)[0x7fdd2d97f34f]\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "/lib64/libpthread.so.0(+0x82f8)[0x7fdd2d9802f8]\n",
      "/lib64/libc.so.6(clone+0x6d)[0x7fdd2cfa9fad]\n",
      "======= Memory map: ========\n",
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      "7fd8b4026000-7fd8b8000000 ---p 00000000 00:00 0 \n",
      "7fd8b8279000-7fd8b87fd000 rw-p 00000000 00:00 0 \n",
      "7fd8b87fd000-7fd8b87fe000 ---p 00000000 00:00 0 \n",
      "7fd8b87fe000-7fd8b8ffe000 rw-p 00000000 00:00 0 \n",
      "7fd8b8ffe000-7fd8b8fff000 ---p 00000000 00:00 0 \n",
      "7fd8b8fff000-7fd8b97ff000 rw-p 00000000 00:00 0 \n",
      "7fd8b97ff000-7fd8b9800000 ---p 00000000 00:00 0 \n",
      "7fd8b9800000-7fd8ba000000 rw-p 00000000 00:00 0 \n",
      "7fd8ba000000-7fd8bb200000 ---p 00000000 00:00 0 \n",
      "7"
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Eval:   0%|          | 0/28 [00:01<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/5 --  train_loss = 0.429608 --  valid_loss = 0.380922 --  time = 558.582504s\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7428b7c8e6254ea78c013b9c18cec316",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training:   0%|          | 0/249 [00:01<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "208a41eb3edc44b681e2070858efbcfb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Eval:   0%|          | 0/28 [00:01<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/5 --  train_loss = 0.386815 --  valid_loss = 0.349619 --  time = 560.903462s\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b63a1849ecef4f1a80356b934fab96d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training:   0%|          | 0/249 [00:01<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eaf7ce6bff8d4e6e96f7d4eb8d810b0c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Eval:   0%|          | 0/28 [00:01<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/5 --  train_loss = 0.363551 --  valid_loss = 0.349730 --  time = 542.344814s\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4e8f87723c74f908a2749ac063a54c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training:   0%|          | 0/249 [00:01<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "412a003f7670477a905a719442dfcd35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Eval:   0%|          | 0/28 [00:01<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "======= Backtrace: =========\n",
      "/lib64/libc.so.6(+0x7340f)[0x7fdd2cf3640f]\n",
      "/lib64/libc.so.6(+0x78c7e)[0x7fdd2cf3bc7e]\n",
      "/lib64/ld-linux-x86-64.so.2(_dl_deallocate_tls+0x39)[0x7fdd2dba7589]\n",
      "/lib64/libpthread.so.0(+0x7237)[0x7fdd2d97f237]\n",
      "/lib64/libpthread.so.0(+0x734f)[0x7fdd2d97f34f]\n",
      "/lib64/libpthread.so.0(+0x82f8)[0x7fdd2d9802f8]\n",
      "/lib64/libc.so.6(clone+0x6d)[0x7fdd2cfa9fad]\n",
      "======= Memory map: ========\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "00400000-0089d000 r-xp 00000000 08:03 36090448                           /home/wangcx/python3.12/bin/python3.12\n",
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      "7fdb9995b000-7fdb99b5b000 ---p 000a0000 08:03 36214612                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libcudart.7ec1eba6.so.12\n",
      "7fdb99b5b000-7fdb99b60000 r--p 000a0000 08:03 36214612                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libcudart.7ec1eba6.so.12\n",
      "7fdb99b60000-7fdb99b61000 rw-p 000a5000 08:03 36214612                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libcudart.7ec1eba6.so.12\n",
      "7fdb99b61000-7fdb99b62000 rw-p 00000000 00:00 0 \n",
      "7fdb99b62000-7fdb9a19d000 r-xp 00000000 08:03 36214613                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libnvjpeg.f00ca762.so.12\n",
      "7fdb9a19d000-7fdb9a39d000 ---p 0063b000 08:03 36214613                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libnvjpeg.f00ca762.so.12\n",
      "7fdb9a39d000-7fdb9a3aa000 r--p 0063b000 08:03 36214613                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libnvjpeg.f00ca762.so.12\n",
      "7fdb9a3aa000-7fdb9a3ab000 rw-p 00648000 08:03 36214613                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libnvjpeg.f00ca762.so.12\n",
      "7fdb9a3ab000-7fdb9a3b0000 rw-p 00000000 00:00 0 \n",
      "7fdb9a3b0000-7fdb9a3f3000 r-xp 00000000 08:03 36214614                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libjpeg.ceea7512.so.62\n",
      "7fdb9a3f3000-7fdb9a5f3000 ---p 00043000 08:03 36214614                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libjpeg.ceea7512.so.62\n",
      "7fdb9a5f3000-7fdb9a5f4000 r--p 00043000 08:03 36214614                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libjpeg.ceea7512.so.62\n",
      "7fdb9a5f4000-7fdb9a5f5000 rw-p 00044000 08:03 36214614                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision.libs/libjpeg.ceea7512.so.62\n",
      "7fdb9a5f5000-7fdb9a605000 rw-p 00000000 00:00 0 \n",
      "7fdb9a605000-7fdb9a613000 r--p 00000000 08:03 36214618                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision/image.so\n",
      "7fdb9a613000-7fdb9a62d000 r-xp 0000e000 08:03 36214618                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision/image.so\n",
      "7fdb9a62d000-7fdb9a633000 r--p 00028000 08:03 36214618                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision/image.so\n",
      "7fdb9a633000-7fdb9a634000 ---p 0002e000 08:03 36214618                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision/image.so\n",
      "7fdb9a634000-7fdb9a635000 r--p 0002e000 08:03 36214618                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision/image.so\n",
      "7fdb9a635000-7fdb9a636000 rw-p 0002f000 08:03 36214618                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision/image.so\n",
      "7fdb9a636000-7fdb9a656000 rw-p 0003c000 08:03 36214618                   /home/wangcx/.venv/lib/python3.12/site-packages/torchvision/image.so\n",
      "7fdb9a656000-7fdb9a671000 r-xp 00000000 08:02 3427087                    /usr/lib64/libfribidi.so.0.4.0\n",
      "7fdb9a671000-7fdb9a870000 ---p 0001b000 08:02 3427087                    /usr/lib64/libfribidi.so.0.4.0\n",
      "7fdb9a870000-7fdb9a871000 r--p 0001a000 08:02 3427087                    /usr/lib64/libfribidi.so.0.4.0\n",
      "7fdb9a871000-7fdb9a872000 rw-p 0001b000 08:02 3427087                    /usr/lib64/libfribidi.so.0.4.0\n",
      "7fdb9a872000-7fdb9a87d000 r--p 00000000 08:03 36053192                   /home/wangcx/.venv/lib/python3.12/site-packages/pillow.libs/libharfbuzz-59c4b977.so.0.60840.0\n",
      "7fdb9a87d000-7fdb9aa4c000 r-xp 0000b000 08:03 36053192                   /home/wangcx/.venv/lib/python3.12/site-packages/pillow.libs/libharfbuzz-59c4b977.so.0.60840.0\n",
      "7fdb9aa4c000-7fdb9ab99000 r--p 001da000 08:03 36053192                   /home/wangcx/.venv/lib/python3.12/site-packages/pillow.libs/libharfbuzz-59c4b977.so.0.60840.0\n",
      "7fdb9ab99000-7fdb9ab9a000 r--p 00326000 08:03 36053192                   /home/wangcx/.venv/lib/python3.12/site-packages/pillo*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "*** Error in `/home/wangcx/.venv/bin/python': munmap_chunk(): invalid pointer: 0x0000000000ba1968 ***\n",
      "======= Backtrace: =========\n",
      "/lib64/libc.so.6(+0x7340f)[0x7fdd2cf3640f]\n",
      "/lib64/libc.so.6(+0x78c7e)[0x7fdd2cf3bc7e]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 --  train_loss = 0.357923 --  valid_loss = 0.349717 --  time = 540.355142s\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_loader = DataLoader(\n",
    "    train_ds,\n",
    "    batch_size=BS,\n",
    "    sampler=train_sampler,\n",
    "    pin_memory=True,\n",
    "    num_workers=20,\n",
    ")\n",
    "valid_loader = DataLoader(\n",
    "    train_ds,\n",
    "    batch_size=1,\n",
    "    sampler=valid_sampler,\n",
    "    pin_memory=True,\n",
    "    num_workers=20,\n",
    ")\n",
    "for epoch in range(1, EPOCHS + 1):\n",
    "    train_loss = 0.0\n",
    "    n_tot_chunks = 0\n",
    "    pbar = tqdm(\n",
    "        train_loader, desc=\"Training\", unit=\"batch\"\n",
    "    )\n",
    "    model.train()\n",
    "    for step,(X_batch, y_batch) in enumerate(pbar):\n",
    "        y_batch = y_batch.to(device, non_blocking=True)\n",
    "        pred = torch.zeros(y_batch.shape).to(device, non_blocking=True)\n",
    "        optimizer.zero_grad()\n",
    "        scheduler.step(step+train_size*epoch)\n",
    "        h = None\n",
    "\n",
    "        seq_len = X_batch.shape[1]\n",
    "        for i in range(0, seq_len, max_chunk_size):\n",
    "            X_chunk = X_batch[:, i : i + max_chunk_size].float()\n",
    "\n",
    "            X_chunk = X_chunk.to(device, non_blocking=True)\n",
    "\n",
    "            y_pred, h = model(X_chunk, h)\n",
    "            h = [hi.detach() for hi in h]\n",
    "            pred[:, i : i + max_chunk_size] = y_pred\n",
    "            del X_chunk,y_pred\n",
    "\n",
    "        loss = criterion(\n",
    "            normalize(pred).float(),\n",
    "            y_batch.float(),\n",
    "        )\n",
    "        loss.backward()\n",
    "        train_loss += loss.item()\n",
    "        n_tot_chunks+=1\n",
    "        pbar.set_description(f'Training: loss = {(train_loss/n_tot_chunks):.2f}')\n",
    "\n",
    "        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1e-1)\n",
    "        optimizer.step()\n",
    "        del pred,loss,y_batch,X_batch,h\n",
    "        gc.collect()\n",
    "    train_loss /= len(train_loader)\n",
    "    del pbar\n",
    "    gc.collect()\n",
    "    ctypes.CDLL(\"libc.so.6\").malloc_trim(0)\n",
    "\n",
    "    if epoch % 1 == 0:\n",
    "        valid_loss = evaluate(\n",
    "            model, max_chunk_size, valid_loader, device, criterion\n",
    "        )\n",
    "\n",
    "        history[\"train_loss\"].append(train_loss)\n",
    "        history[\"valid_loss\"].append(valid_loss)\n",
    "        history[\"lr\"].append(optimizer.param_groups[0][\"lr\"])\n",
    "\n",
    "        if valid_loss < best_valid_loss:\n",
    "            best_valid_loss = valid_loss\n",
    "            torch.save(\n",
    "                model.state_dict(),\n",
    "                os.path.join(model_path, f\"model_best.pth\"),\n",
    "            )\n",
    "\n",
    "        dt = time.time() - dt\n",
    "        print(\n",
    "            f\"{epoch}/{EPOCHS} -- \",\n",
    "            f\"train_loss = {train_loss:.6f} -- \",\n",
    "            f\"valid_loss = {valid_loss:.6f} -- \",\n",
    "            f\"time = {dt:.6f}s\",\n",
    "        )\n",
    "        dt = time.time()\n",
    "\n",
    "plot_history(history, model_path=model_path)\n",
    "history_path = os.path.join(model_path, \"history.json\")\n",
    "with open(history_path, \"w\", encoding=\"utf-8\") as f:\n",
    "    json.dump(history, f, ensure_ascii=False, indent=4)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
   "id": "b465fb58",
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       "{'train_loss': [0.7537940854737797,\n",
       "  0.4296077048204031,\n",
       "  0.3868149807714075,\n",
       "  0.36355065329965336,\n",
       "  0.3579226799877772],\n",
       " 'valid_loss': [0.46175698989204,\n",
       "  0.3809224601302828,\n",
       "  0.3496185378836734,\n",
       "  0.3497299341750996,\n",
       "  0.3497165728892599],\n",
       " 'valid_mAP': [],\n",
       " 'lr': [0.0006557148224444123,\n",
       "  0.00034670499220408856,\n",
       "  9.625246429623541e-05,\n",
       "  2.159181332111897e-08,\n",
       "  2e-08]}"
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